The rapid progress in global port construction has resulted in a significant rise in container throughput. As the development of automated container terminals continues, it is imperative to enhance the intelligence and environmental friendliness of Automated Guided Vehicles (AGVs). This paper focuses on addressing the AGV path planning problem by employing deep reinforcement learning. The primary objectives are to optimize data collection efficiency and minimize battery consumption. The diverse layouts of terminal environments are represented using RGB images, and path learning is incorporated with a meticulously designed reward function. Here, we perform 1000 simulations using Monte Carlo to evaluate the parameters for these four scenarios. The evaluation showed that the terminal path planning battery consumption averaged around 40 to 56, the data collection rates were all above 80% on average, and more than 99% of them resulted in optimal solutions in a 32×32 size environment. The algorithm is able to reach and collect the data as fast as possible.
AGV path planning and optimization with deep reinforcement learning model
04.08.2023
1126631 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Deep Reinforcement Learning-Based Local Path Planning with Memory-Guided
Springer Verlag | 2025
|UAV path planning with kinematic constraints based on deep reinforcement learning
British Library Conference Proceedings | 2022
|Unmanned Aerial Vehicles Path Planning Based on Deep Reinforcement Learning
Springer Verlag | 2019
|